scholarly journals The true worth of a nurse … time to act!

2020 ◽  
Vol 76 (10) ◽  
pp. 2469-2470 ◽  
Author(s):  
Ged Williams
Keyword(s):  
Nurse Time

scholarly journals Improving Nurse Staffing Measures: Discharge Day Measurement in “Adjusted Patient Days of Care”

2003 ◽  
Vol 40 (3) ◽  
pp. 295-304 ◽  
Author(s):  
Lynn Y. Unruh ◽  
Myron D. Fottler ◽  
Laura L. Talbott
Keyword(s):  
Empirical Studies ◽  
Nurse Staffing ◽  
Lengths Of Stay ◽  
Significant Difference ◽  
Patient Load ◽  
Before And After ◽  
Nurse Time ◽  
Conceptual Problems ◽  
The Difference ◽  
Using Data

Previous research cannot account for the discrepancy between registered nurse (RN) reports of understaffing and studies showing slight improvement. One reason may be that “adjusted patient days of care”(APDC) underestimates patient load. Using data from all Pennsylvania acute care general hospitals for the years 1994 through 1997, we found that APDC is underestimated by two hours. After adjusting APDC, we examined the difference in nurse staffing over the period 1991–2000 before and after the adjustment. We found a significant difference between unadjusted and adjusted measures. However, when applied to the changes in nurse staffing between 1991 and 2000, the difference was not enough to account for the discrepancy between reports and data. Other measurement and conceptual problems may exist in terms of patients' increasing acuity levels, patients' declining lengths of stay and the associated greater proportion of nurse time devoted to admission and discharge, and lack of recent data in some empirical studies.

scholarly journals Changes to physician and nurse time burdens when caring for patients under contact precautions

2017 ◽  
Vol 45 (5) ◽  
pp. 542-543 ◽  
Author(s):  
Anna K. Barker ◽  
James Codella ◽  
Tola Ewers ◽  
Adam Dundon ◽  
Oguzhan Alagoz ◽  
...  
Keyword(s):  
Contact Precautions ◽  
Nurse Time

Editorial: Call yourself a nurse? Time to get precious

2012 ◽  
Vol 21 (19pt20) ◽  
pp. 2809-2811
Author(s):  
Claudine Wetherall
Keyword(s):  
Nurse Time

scholarly journals The electronic health record audit file: the patient is waiting

2016 ◽  
Vol 24 (e1) ◽  
pp. e28-e34 ◽  
Author(s):  
Annemarie G Hirsch ◽  
J B Jones ◽  
Virginia R Lerch ◽  
Xiaoqin Tang ◽  
Andrea Berger ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Wait Time ◽  
Clinical Encounter ◽  
Health Record ◽  
Clinical Workflow ◽  
Waiting Room ◽  
Exam Room ◽  
Physician Time ◽  
Nurse Time ◽  
Electronic Health

Objective: We describe how electronic health record (EHR) audit files can be used to understand how time is spent in primary care (PC). Materials/methods: We used audit file data from the Geisinger Clinic to quantify elements of the clinical workflow and to determine how these times vary by patient and encounter factors. We randomly selected audit file records representing 36 437 PC encounters across 26 clinic locations. Audit file data were used to estimate duration and variance of: (1) time in the waiting room, (2) nurse time with the patient, (3) time in the exam room without a nurse or physician, and (4) physician time with the patient. Multivariate modeling was used to test for differences by patient and by encounter features. Results: On average, a PC encounter took 54.6 minutes, with 5 minutes of nurse time, 15.5 minutes of physician time, and the remaining 62% of the time spent waiting to see a clinician or check out. Older age, female sex, and chronic disease were associated with longer wait times and longer time with clinicians. Level of service and numbers of medications, procedures, and lab orders were associated with longer time with clinicians. Late check-in and same-day visits were associated with shorter wait time and clinician time. Conclusions: This study provides insights on uses of audit file data for workflow analysis during PC encounters. Discussion: Scalable ways to quantify clinical encounter workflow elements may provide the means to develop more efficient approaches to care and improve the patient experience.

scholarly journals Development of Feature Based Classification of Fruit using Deep Learning

2019 ◽  
Vol 8 (12) ◽  
pp. 3285-3290 ◽  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Health Services ◽  
System Dynamics ◽  
School Health ◽  
School Health Services ◽  
System Dynamics Model ◽  
Dynamics Model ◽  
Nurse Time ◽  
Time Required

One of the national primary health care services in Malaysia is school health care. This care is very crucial as it ensures that, countrywide, the health of students from the age of five to fifteen is in a good condition. In Malaysia, nurses hold a major responsibility for delivering the school health service. However, there is no solid research investigating the nursing time required to deliver school health services. This paper presents a system dynamics model representing the specific school health services delivered by nurses. System Dynamics is a computer-aided approach to policy analysis and design. In this paper, the system dynamics model are represented by several causal loop diagrams which covers all the school health activities and is able to determine the projected total nurse time required in delivering the service. The baseline simulation result of the nurse time required for delivering school health services is about 1080000 hours in year 2030, which is equivalent to 680 full time equivalent (FTE) nurses. Furthermore, various what-if analyses are tested with the model, as it is important for policy makers to investigate various scenarios for an effective decision-making process. In other words, the theme of the study is to understand the implication of the changes in school population size and the modification of certain activities in the school health program on the nurse time spent delivering school health service by developing a dedicated forecasting system dynamics model for school health. The time horizon for the forecasting is from 2018 until 2030Fruit classification is a challenging task in image processing. Computer vision based classification method is agile and rigorous compared to human based approach. In this paper, a method is developed for feature classification using deep learning. The region with their own characteristics is classified based on deep learning convolutional neural network technique. Traditional method for diagnosis of fruit involves visual observations by experts. The interference of environmental factors needs to be considered during diagnosis process. Datasets such as VOC, PASCAL, ImageNet etc. are easily available that are used for training of several different types of objects. The proposed model introduces two pre-trained networks; AlexNet and GoogLeNet. For faster and optimized training, Rectified linear unit (ReLu) is used that maintain positive value and map negative values to zero. The model learns to perform classification directly from images. Neural network architecture is used for implementation of deep learning. Error in deep learning is minimized compared to machine learning. The high end GPU’s reduces the training time. A transfer learning technique is proposed to retrain the network that is capable of performing new recognition task.

scholarly journals Home Care of High Risk Pregnant Women by Advanced Practice Nurses: Nurse Time Consumed

1998 ◽  
Vol 16 (12) ◽  
pp. 823-830 ◽  
Author(s):  
Dorothy Brooten ◽  
Latina Brooks ◽  
Elizabeth A. Madigan ◽  
JoAnne M. Youngblut
Keyword(s):  
High Risk ◽  
Home Care ◽  
Pregnant Women ◽  
Advanced Practice Nurses ◽  
Advanced Practice ◽  
Practice Nurses ◽  
Nurse Time

Drug use and pressure ulcers in long-term care units: do nurse time pressure and unfair management increase the prevalence?

2008 ◽  
Vol 17 (22) ◽  
pp. 3067-3073 ◽  
Author(s):  
Laura Pekkarinen ◽  
Timo Sinervo ◽  
Marko Elovainio ◽  
Anja Noro ◽  
Harriet Finne-Soveri
Keyword(s):  
Drug Use ◽  
Long Term Care ◽  
Pressure Ulcers ◽  
Time Pressure ◽  
Term Care ◽  
Nurse Time

Feasibility of a Symptom Management Intervention for Honduran Adults Undergoing Chemotherapy

2019 ◽  
Vol 41 (10) ◽  
pp. 1517-1539 ◽  
Author(s):  
Suyapa Bejarano ◽  
Meghan E. Freed ◽  
Darwin Zeron ◽  
Rennie Medina ◽  
Julio Cesar Zuniga-Moya ◽  
...  
Keyword(s):  
Symptom Management ◽  
Quantitative Data ◽  
Treatment Manual ◽  
Evidence Based ◽  
Middle Income ◽  
Qualitative And Quantitative ◽  
Management Intervention ◽  
Middle Income Countries ◽  
Different Types ◽  
Nurse Time

Evidence-based interventions often need to be adapted to maximize their implementation potential in low-to middle-income countries. A single-arm feasibility study was conducted to determine the feasibility and acceptability of a telephone-delivered, nurse-led, symptom management intervention for adults undergoing chemotherapy in Honduras. Over the course of 6 months, nurses engaged 25 patients undergoing chemotherapy in the intervention. Each participant received an average of 16.2 attempts to contact them for telephone sessions ( SD = 8.0, range = 2-28). Collectively, the participants discussed 24 different types of symptoms. The most commonly discussed symptoms were pain (12%), nausea (7%), and constipation (5%). Qualitative and quantitative data were used to identify treatment manual modifications (i.e., adding content about different symptoms and addressing scheduling of treatment) and workplace modifications (i.e., dedicated nurse time and space) that are needed to optimize implementation of the intervention.

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